An edge within an image is a sharp change in local intensity or lightness. In other words, edges are features within an image that possess strong intensity contrast. Edges occur between distinct objects in a scene, or within textures and structure within an object. For instance, typographic characters on a white page background produce distinct edges. Edge pixels in a digital image are those pixels that occur at and about an edge in the image.
Two key properties of an edge are strength and orientation. Edge strength is a measure of the contrast of an edge. A black typographic character on a white background produces stronger edges than a gray character on a white background. Edge orientation can be described by a variety of measures, such as angle quantified in degrees or by classes such as vertical, horizontal, and diagonal.
In order to produce crisp text and graphics, current xerographic printers detect text and line edge and render them differently than by applying the standard line or clustered dot screen. Significant previous art exists for algorithms of detecting text and line art edge. One common term for this family of methods is anti-aliased tagging (AAT). This technology, which is a detection methodology, is used in conjunction with a rendering technology. In other words, the halftoning method applied to edges detected from AAT differs from the normal method applied to the rest of the image or document. The unique edge based halftoning method applied to the edges is similarly referred to as anti-aliased rendering (AAR). In high addressable systems—systems where the number of output pixels (or locations) is a multiple greater than 1 of the input pixels—the edge pixels might be generated using either a LUT (predefined mapping) or a localized screen.
FIG. 1 illustrates a schematic diagram of a prior art example of an input image 12 at 600 spi, a tagged image 14, and the possible resulting output image 16 at 1200×1200 using AAT and AAR. The input image 12 is a segment of a black line (on the right) in the white background. Although not shown, the line is a vertical line that continues above and below the shown context. The input image 12 has a gray boundary between the white and black areas. The AAT algorithm detects the gray pixels as edge pixels aligned left-right with the dark side on the right. The AAT renderer then created a 2×2 binary output for each gray pixel where the two left pixels are white and the two right pixels are black. This results in text that effectively appears to be created at the output resolution of 1200 with an input “gray” resolution of 600 spi. Although not shown here, the AAT and AAR paradigm is also effectively used to outline gray text to significantly improve apparent edge sharpness.
There are two major problems with this aforementioned method. The first is that in some xerographic systems the response to character outlining is highly non-linear. For an edge pixel turning on only one subpixel of a 2×2 output pixel may barely outline while turning on two of the subpixels may create an outline that is overly dark. It is therefore hard to control the edge outline strength accurately in these systems. Additionally, at such small scales the response from engine to engine may differ significantly making the method non-repeatable over a fleet of printers. Furthermore, the current AAT method is only useful in systems that are high addressable. If there are no subpixels to manipulate, the current method has only binary control over a character outline—either it is on or off everywhere. This precludes use of AAT/AAR in inkjet based devices where the output image resolution is identical to the input image RIP resolution. It would be useful to maintain the current benefits of AAT by extension and require significant redesign of the basic approach.